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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import math |
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import random |
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import numpy as np |
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class InitialSampler: |
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def __init__(self, conv, init_sample_size, dim_max_sample_size=1000000): |
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self.conv = conv |
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self.init_sample_size = init_sample_size |
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self.dim_max_sample_size = dim_max_sample_size |
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def get_pos_space(self): |
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if self.init_sample_size < self.conv.search_space_size: |
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n_samples_array = self.get_n_samples_dims() |
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return self.random_choices(n_samples_array) |
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else: |
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if self.conv.max_dim < 255: |
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_dtype = np.uint8 |
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elif self.conv.max_dim < 65535: |
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_dtype = np.uint16 |
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elif self.conv.max_dim < 4294967295: |
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_dtype = np.uint32 |
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else: |
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_dtype = np.uint64 |
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pos_space = [] |
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for dim_ in self.conv.dim_sizes: |
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pos_space.append(np.arange(dim_, dtype=_dtype)) |
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return pos_space |
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def get_n_samples_dims(self): |
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# TODO of search space is > 33 dims termination criterion must be: |
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# "search_space_size < self.init_sample_size" |
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dim_sizes_temp = self.conv.dim_sizes |
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dim_sizes_temp = np.clip( |
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dim_sizes_temp, a_min=1, a_max=self.dim_max_sample_size |
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) |
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search_space_size = self.conv.dim_sizes.prod() |
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while abs(search_space_size - self.init_sample_size) > 10000: |
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n_samples_array = [] |
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for idx, dim_size in enumerate(np.nditer(dim_sizes_temp)): |
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array_diff_ = random.randint(1, dim_size) |
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n_samples_array.append(array_diff_) |
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sub = int((dim_size / 1000) ** 1.5) |
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dim_sizes_temp[idx] = np.maximum(1, dim_size - sub) |
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search_space_size = np.array(n_samples_array).prod() |
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return n_samples_array |
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def random_choices(self, n_samples_array): |
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pos_space = [] |
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for n_samples, dim_size in zip(n_samples_array, self.conv.dim_sizes): |
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if dim_size > self.dim_max_sample_size: |
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pos_space.append( |
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np.random.randint(low=1, high=dim_size, size=n_samples) |
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) |
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else: |
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pos_space.append( |
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np.random.choice(dim_size, size=n_samples, replace=False) |
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) |
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return pos_space |
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SimonBlanke /
Gradient-Free-Optimizers
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